{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "408bbe97",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pylab import mpl\n",
    "mpl.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "mpl.rcParams[\"axes.unicode_minus\"] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9d2aba92",
   "metadata": {},
   "outputs": [],
   "source": [
    "def data2Dict(arr):\n",
    "    \"\"\"把数组转换成字典，并统计数量\"\"\"\n",
    "    key = np.unique(arr)\n",
    "    result = {}\n",
    "    for k in key:\n",
    "        arrNew=arr[arr==k]\n",
    "        result[k]=arrNew.size\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3bcfec01",
   "metadata": {},
   "outputs": [],
   "source": [
    "def data2Dict2(arr):\n",
    "    \"\"\"把数组转换成字典，并统计数量\"\"\"\n",
    "    key = np.unique(arr)\n",
    "    result = {}\n",
    "    list=[]\n",
    "    for k in key:\n",
    "        arrNew.append(arr[arr==k])\n",
    "        result[k]=len(arrNew)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "88b0155f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>system_id</th>\n",
       "      <th>system_name</th>\n",
       "      <th>purchase_project_name</th>\n",
       "      <th>purchase_project_code</th>\n",
       "      <th>purchase_project_type</th>\n",
       "      <th>purchase_project_type_mean</th>\n",
       "      <th>contactor</th>\n",
       "      <th>contact_information</th>\n",
       "      <th>...</th>\n",
       "      <th>status_mean</th>\n",
       "      <th>data_create_time</th>\n",
       "      <th>data_modify_time</th>\n",
       "      <th>is_two_envelope</th>\n",
       "      <th>is_two_envelope_mean</th>\n",
       "      <th>is_dark</th>\n",
       "      <th>is_dark_mean</th>\n",
       "      <th>judge_way</th>\n",
       "      <th>judge_way_name</th>\n",
       "      <th>type_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>中大招（工）2021-0916号</td>\n",
       "      <td>B</td>\n",
       "      <td>工程类</td>\n",
       "      <td>游舒萍</td>\n",
       "      <td>13760330460</td>\n",
       "      <td>...</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>16/9/2021 10:34:20</td>\n",
       "      <td>16/9/2021 10:34:20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>综合评分法</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18167E7079C140A2A61F81059E9DFA1B</td>\n",
       "      <td>0ae40d89-e220-428e-b468-a18827499517</td>\n",
       "      <td>NaN</td>\n",
       "      <td>中山电子招投标系统</td>\n",
       "      <td>中山大学精准医学科学中心服务器及网络设备采购项目</td>\n",
       "      <td>中大招（货）[2019]303号</td>\n",
       "      <td>A</td>\n",
       "      <td>货物类</td>\n",
       "      <td>lzw</td>\n",
       "      <td>020-84115085</td>\n",
       "      <td>...</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>16/9/2021 11:05:20</td>\n",
       "      <td>16/9/2021 11:05:20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>综合评分法</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5E06739D7B9D424C894820065BB7F2A1</td>\n",
       "      <td>84929838-3d94-4193-96f7-6a29ff8b73f7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>中山电子招投标系统</td>\n",
       "      <td>中山大学广州校区南校园中山楼一期网络布线系统改造采购项目</td>\n",
       "      <td>中大招（服）[2019]072号</td>\n",
       "      <td>C</td>\n",
       "      <td>服务类</td>\n",
       "      <td>lzw</td>\n",
       "      <td>020-84115085</td>\n",
       "      <td>...</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>16/9/2021 11:57:27</td>\n",
       "      <td>16/9/2021 11:57:27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>综合评分法</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89BA79F769C34A388E2ED77BD13DBB24</td>\n",
       "      <td>cf2a05b6-191f-49b2-8568-5e863f8b7e03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>中山电子招投标系统</td>\n",
       "      <td>中山大学2021年南校园474栋中山楼南侧生态停车场改造工程</td>\n",
       "      <td>中大招（工） [2021]014 号</td>\n",
       "      <td>B</td>\n",
       "      <td>工程类</td>\n",
       "      <td>陶亮</td>\n",
       "      <td>13710639056</td>\n",
       "      <td>...</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>16/9/2021 15:21:41</td>\n",
       "      <td>16/9/2021 15:24:05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>综合评分法</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9F1C9DE4BB0E423CAA7BC1226E1B0BB0</td>\n",
       "      <td>ee05b9e4-a63c-4d08-8832-bccb2ff1e0ab</td>\n",
       "      <td>NaN</td>\n",
       "      <td>中山电子招投标系统</td>\n",
       "      <td>中山大学2021年南校园474栋中山楼南侧生态停车场改造工程</td>\n",
       "      <td>中大招（工） [2021]014 号</td>\n",
       "      <td>B</td>\n",
       "      <td>工程类</td>\n",
       "      <td>陶亮</td>\n",
       "      <td>13710639056</td>\n",
       "      <td>...</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>16/9/2021 17:20:48</td>\n",
       "      <td>16/9/2021 17:31:11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>综合评分法</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>96824E1963C04305ABFB75B95C85EDFE</td>\n",
       "      <td>20153f94-a8ed-4726-ae45-209db635c34b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>中山电子招投标系统</td>\n",
       "      <td>中山大学广州校区南校园与东校园教学楼空调更换采购项目</td>\n",
       "      <td>中大招（货）[2023]124号</td>\n",
       "      <td>A</td>\n",
       "      <td>货物类</td>\n",
       "      <td>范华平</td>\n",
       "      <td>020-84115089</td>\n",
       "      <td>...</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>1/8/2023 09:30:54</td>\n",
       "      <td>1/8/2023 09:30:59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>综合评分法</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>B74AAA60221C4E26A4977D0BADC23280</td>\n",
       "      <td>23049233-cd8c-4ab8-9ee4-c4a8b9f38e5b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>中山电子招投标系统</td>\n",
       "      <td>中山大学南校园怡乐路教师公寓网络设备采购项目</td>\n",
       "      <td>中大招（货）[2023]115号</td>\n",
       "      <td>A</td>\n",
       "      <td>货物类</td>\n",
       "      <td>范华平</td>\n",
       "      <td>020-84115085</td>\n",
       "      <td>...</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>4/8/2023 09:30:29</td>\n",
       "      <td>4/8/2023 09:30:29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>综合评分法</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>74AF8C1818AD406A96F959103BA0946D</td>\n",
       "      <td>8a030df8-54bc-47e1-be32-b7d1b4108758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>中山电子招投标系统</td>\n",
       "      <td>中山大学广州校区南校园黄铭衍堂黄传经堂林护堂课室改造项目之多媒体及标准化考场设备采购项目</td>\n",
       "      <td>中大招（货）[2023]101号</td>\n",
       "      <td>A</td>\n",
       "      <td>货物类</td>\n",
       "      <td>李亚珍</td>\n",
       "      <td>18902235313</td>\n",
       "      <td>...</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>10/8/2023 09:30:16</td>\n",
       "      <td>10/8/2023 09:30:18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>综合评分法</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>A38C772C3A1E43E880F66721A2359243</td>\n",
       "      <td>e420faad-b3b9-4405-88a2-5a89fcf4f72c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>中山电子招投标系统</td>\n",
       "      <td>中山大学五校园消防器材采购项目</td>\n",
       "      <td>中大招（货）[2023]069号</td>\n",
       "      <td>A</td>\n",
       "      <td>货物类</td>\n",
       "      <td>李艳媚</td>\n",
       "      <td>020-84115091</td>\n",
       "      <td>...</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>10/8/2023 09:30:43</td>\n",
       "      <td>10/8/2023 09:30:43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>综合评分法</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>10895A88C81C4FD0923CD515E90C66F5</td>\n",
       "      <td>9d2d1b97-4b88-4ec7-8595-d946809f30e9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>中山电子招投标系统</td>\n",
       "      <td>中山大学2023年北校园公共卫生学院科研实验室搬迁服务采购项目</td>\n",
       "      <td>中大招（服）[2023]127号</td>\n",
       "      <td>C</td>\n",
       "      <td>服务类</td>\n",
       "      <td>李亚珍</td>\n",
       "      <td>020-84115091</td>\n",
       "      <td>...</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>18/8/2023 09:31:09</td>\n",
       "      <td>18/8/2023 09:31:09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>综合评分法</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1234 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    id                             source_id  \\\n",
       "0     54CCF6E5AC9E46A482EC900AB81E6F86  98f5af2e-8f9e-4177-986b-beb1be6bfdb0   \n",
       "1     18167E7079C140A2A61F81059E9DFA1B  0ae40d89-e220-428e-b468-a18827499517   \n",
       "2     5E06739D7B9D424C894820065BB7F2A1  84929838-3d94-4193-96f7-6a29ff8b73f7   \n",
       "3     89BA79F769C34A388E2ED77BD13DBB24  cf2a05b6-191f-49b2-8568-5e863f8b7e03   \n",
       "4     9F1C9DE4BB0E423CAA7BC1226E1B0BB0  ee05b9e4-a63c-4d08-8832-bccb2ff1e0ab   \n",
       "...                                ...                                   ...   \n",
       "1229  96824E1963C04305ABFB75B95C85EDFE  20153f94-a8ed-4726-ae45-209db635c34b   \n",
       "1230  B74AAA60221C4E26A4977D0BADC23280  23049233-cd8c-4ab8-9ee4-c4a8b9f38e5b   \n",
       "1231  74AF8C1818AD406A96F959103BA0946D  8a030df8-54bc-47e1-be32-b7d1b4108758   \n",
       "1232  A38C772C3A1E43E880F66721A2359243  e420faad-b3b9-4405-88a2-5a89fcf4f72c   \n",
       "1233  10895A88C81C4FD0923CD515E90C66F5  9d2d1b97-4b88-4ec7-8595-d946809f30e9   \n",
       "\n",
       "      system_id system_name                         purchase_project_name  \\\n",
       "0           NaN   中山电子招投标系统                       公开招标-委托代理机构-20210916-01   \n",
       "1           NaN   中山电子招投标系统                      中山大学精准医学科学中心服务器及网络设备采购项目   \n",
       "2           NaN   中山电子招投标系统                  中山大学广州校区南校园中山楼一期网络布线系统改造采购项目   \n",
       "3           NaN   中山电子招投标系统                中山大学2021年南校园474栋中山楼南侧生态停车场改造工程   \n",
       "4           NaN   中山电子招投标系统                中山大学2021年南校园474栋中山楼南侧生态停车场改造工程   \n",
       "...         ...         ...                                           ...   \n",
       "1229        NaN   中山电子招投标系统                    中山大学广州校区南校园与东校园教学楼空调更换采购项目   \n",
       "1230        NaN   中山电子招投标系统                        中山大学南校园怡乐路教师公寓网络设备采购项目   \n",
       "1231        NaN   中山电子招投标系统  中山大学广州校区南校园黄铭衍堂黄传经堂林护堂课室改造项目之多媒体及标准化考场设备采购项目   \n",
       "1232        NaN   中山电子招投标系统                               中山大学五校园消防器材采购项目   \n",
       "1233        NaN   中山电子招投标系统               中山大学2023年北校园公共卫生学院科研实验室搬迁服务采购项目   \n",
       "\n",
       "     purchase_project_code purchase_project_type purchase_project_type_mean  \\\n",
       "0         中大招（工）2021-0916号                     B                        工程类   \n",
       "1         中大招（货）[2019]303号                     A                        货物类   \n",
       "2         中大招（服）[2019]072号                     C                        服务类   \n",
       "3       中大招（工） [2021]014 号                     B                        工程类   \n",
       "4       中大招（工） [2021]014 号                     B                        工程类   \n",
       "...                    ...                   ...                        ...   \n",
       "1229      中大招（货）[2023]124号                     A                        货物类   \n",
       "1230      中大招（货）[2023]115号                     A                        货物类   \n",
       "1231      中大招（货）[2023]101号                     A                        货物类   \n",
       "1232      中大招（货）[2023]069号                     A                        货物类   \n",
       "1233      中大招（服）[2023]127号                     C                        服务类   \n",
       "\n",
       "     contactor contact_information  ...  status_mean    data_create_time  \\\n",
       "0          游舒萍         13760330460  ...         数据有效  16/9/2021 10:34:20   \n",
       "1          lzw        020-84115085  ...         数据有效  16/9/2021 11:05:20   \n",
       "2          lzw        020-84115085  ...         数据有效  16/9/2021 11:57:27   \n",
       "3           陶亮         13710639056  ...         数据有效  16/9/2021 15:21:41   \n",
       "4           陶亮         13710639056  ...         数据有效  16/9/2021 17:20:48   \n",
       "...        ...                 ...  ...          ...                 ...   \n",
       "1229       范华平        020-84115089  ...         数据有效   1/8/2023 09:30:54   \n",
       "1230       范华平        020-84115085  ...         数据有效   4/8/2023 09:30:29   \n",
       "1231       李亚珍         18902235313  ...         数据有效  10/8/2023 09:30:16   \n",
       "1232       李艳媚        020-84115091  ...         数据有效  10/8/2023 09:30:43   \n",
       "1233       李亚珍        020-84115091  ...         数据有效  18/8/2023 09:31:09   \n",
       "\n",
       "        data_modify_time  is_two_envelope is_two_envelope_mean  is_dark  \\\n",
       "0     16/9/2021 10:34:20              NaN                  NaN      NaN   \n",
       "1     16/9/2021 11:05:20              NaN                  NaN      NaN   \n",
       "2     16/9/2021 11:57:27              NaN                  NaN      NaN   \n",
       "3     16/9/2021 15:24:05              NaN                  NaN      NaN   \n",
       "4     16/9/2021 17:31:11              NaN                  NaN      NaN   \n",
       "...                  ...              ...                  ...      ...   \n",
       "1229   1/8/2023 09:30:59              NaN                  NaN      NaN   \n",
       "1230   4/8/2023 09:30:29              NaN                  NaN      NaN   \n",
       "1231  10/8/2023 09:30:18              NaN                  NaN      NaN   \n",
       "1232  10/8/2023 09:30:43              NaN                  NaN      NaN   \n",
       "1233  18/8/2023 09:31:09              NaN                  NaN      NaN   \n",
       "\n",
       "      is_dark_mean  judge_way  judge_way_name type_flag  \n",
       "0              NaN          2           综合评分法       NaN  \n",
       "1              NaN          2           综合评分法       NaN  \n",
       "2              NaN          2           综合评分法       NaN  \n",
       "3              NaN          2           综合评分法       NaN  \n",
       "4              NaN          2           综合评分法       NaN  \n",
       "...            ...        ...             ...       ...  \n",
       "1229           NaN          2           综合评分法       NaN  \n",
       "1230           NaN          2           综合评分法       NaN  \n",
       "1231           NaN          2           综合评分法       NaN  \n",
       "1232           NaN          2           综合评分法       NaN  \n",
       "1233           NaN          2           综合评分法       NaN  \n",
       "\n",
       "[1234 rows x 41 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "projects = pd.read_csv('D:\\\\data\\\\tdz_project_info.csv')\n",
    "projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3200e591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>类型</th>\n",
       "      <th>数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>工程类</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>服务类</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>货物类</td>\n",
       "      <td>914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    类型   数量\n",
       "0  工程类  174\n",
       "1  服务类  146\n",
       "2  货物类  914"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "projectType = projects['purchase_project_type_mean']\n",
    "typeArr = data2Dict(projectType)\n",
    "# typeArr\n",
    "# 创建DF\n",
    "typeArrFrame=pd.DataFrame({'类型':[x for x in typeArr.keys()],\n",
    "            '数量':[x for x in typeArr.values()]})\n",
    "typeArrFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1ac4793f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = typeArrFrame['类型'] #坐标\n",
    "y = typeArrFrame['数量'] # 数据\n",
    "\n",
    "plt.bar(range(len(typeArrFrame)),y)\n",
    "plt.xticks(range(len(typeArrFrame)),x)\n",
    "plt.ylim(0,1000)\n",
    "plt.title('招投标类型统计表')\n",
    "plt.xlabel('类型')\n",
    "plt.ylabel('数量')\n",
    "\n",
    "for m,n in enumerate(typeArrFrame['数量']):\n",
    "    plt.text(m-0.1,n,\"%s\" %n)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b67e5e60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'平均值法': 69, '综合评分法': 1165}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "way = projects['judge_way_name']\n",
    "wayArr = data2Dict(way)\n",
    "wayArr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "006f7b21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建DF\n",
    "wayArrFrame=pd.DataFrame({'评分方法':[x for x in wayArr.keys()],\n",
    "            '数量':[x for x in wayArr.values()]})\n",
    "\n",
    "x = wayArrFrame['评分方法'] #坐标\n",
    "y = wayArrFrame['数量'] # 数据\n",
    "\n",
    "plt.bar(range(len(wayArrFrame)),y)\n",
    "plt.xticks(range(len(wayArrFrame)),x)\n",
    "plt.ylim(0,1200)\n",
    "plt.title('评分方法统计表')\n",
    "plt.xlabel('评分类型')\n",
    "plt.ylabel('数量')\n",
    "\n",
    "for m,n in enumerate(wayArrFrame['数量']):\n",
    "    plt.text(m-0.1,n,\"%s\" %n)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "629af4d0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'中山医学院': 66,\n",
       " '中山大学': 14,\n",
       " '中山大学天琴引力物理研究中心': 2,\n",
       " '中山大学精准医学科学中心': 1,\n",
       " '中山眼科中心': 1,\n",
       " '中法核工程与技术学院': 15,\n",
       " '中铁建招标人': 2,\n",
       " '人力资源管理处': 1,\n",
       " '人工智能学院': 8,\n",
       " '保卫处（综合治理督察办公室）': 2,\n",
       " '信息化管理办公室': 1,\n",
       " '先进制造学院': 6,\n",
       " '先进能源学院': 12,\n",
       " '光华口腔医学院': 2,\n",
       " '光电材料与技术国家重点实验室': 2,\n",
       " '党委保卫部、保卫处（综合治理督察办公室）': 15,\n",
       " '党委学生工作部': 1,\n",
       " '党委宣传部': 1,\n",
       " '公共卫生学院': 3,\n",
       " '公共卫生学院（深圳）': 9,\n",
       " '公共实验教学中心': 1,\n",
       " '农学院': 10,\n",
       " '化学学院': 29,\n",
       " '化学学院 ': 1,\n",
       " '化学工程与技术学院': 10,\n",
       " '医学院': 35,\n",
       " '商学院': 2,\n",
       " '国际翻译学院': 1,\n",
       " '图书馆': 36,\n",
       " '土木工程学院': 16,\n",
       " '地球科学与地质工程学院': 2,\n",
       " '地球科学与工程学院': 9,\n",
       " '地理科学与规划学院': 9,\n",
       " '基建处': 12,\n",
       " '外国语学院': 2,\n",
       " '大气科学学院': 24,\n",
       " '天琴中心': 74,\n",
       " '审计处': 1,\n",
       " '岭南学院': 1,\n",
       " '广东省地方立法研究评估与咨询服务基地': 1,\n",
       " '张幸鼎': 9,\n",
       " '微电子科学与技术学院': 12,\n",
       " '总务处': 28,\n",
       " '总务部': 136,\n",
       " '政府采购与招投标管理中心': 1,\n",
       " '教务部': 9,\n",
       " '教育部食品工程研究中心': 25,\n",
       " '数学学院（珠海）': 1,\n",
       " '数据科学与计算机学院': 1,\n",
       " '旅游学院': 5,\n",
       " '智能工程学院': 25,\n",
       " '材料学院': 6,\n",
       " '材料科学与工程学院': 16,\n",
       " '核算中心': 1,\n",
       " '测绘科学与技术学院': 33,\n",
       " '测试中心': 44,\n",
       " '海洋工程与技术学院': 17,\n",
       " '海洋科学学院': 5,\n",
       " '深圳校区实验动物中心': 5,\n",
       " '物理与天文学院': 15,\n",
       " '物理学院': 41,\n",
       " '环境科学与工程学院': 13,\n",
       " '理学院': 8,\n",
       " '生命科学学院': 17,\n",
       " '生态学院': 11,\n",
       " '生物医学工程学院': 13,\n",
       " '电子与信息工程学院（微电子学院）': 44,\n",
       " '电子与通信工程学院': 27,\n",
       " '碧鼎技术部': 51,\n",
       " '科学研究院': 21,\n",
       " '管理学院': 3,\n",
       " '管理学院（创业学院）': 1,\n",
       " '系统科学与工程学院': 10,\n",
       " '网络与信息中心': 28,\n",
       " '网络与信息技术中心': 2,\n",
       " '网络空间安全学院': 12,\n",
       " '胡胜楠': 1,\n",
       " '航空航天学院': 25,\n",
       " '药学院': 9,\n",
       " '药学院（深圳）': 14,\n",
       " '计算机学院（软件学院）': 11,\n",
       " '设备与实验室管理处': 4,\n",
       " '财务处': 1,\n",
       " '软件工程学院': 1,\n",
       " '附属第七医院': 2,\n",
       " '附属第三医院（第三临床学院）': 3,\n",
       " '附属第五医院': 2,\n",
       " '附属第六医院': 7,\n",
       " '集成电路学院': 18}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase=data2Dict(projects['purchase_name'])\n",
    "purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "cf6a62ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建DF\n",
    "purchaseFrame=pd.DataFrame({'采购部门':[x for x in purchase.keys()],\n",
    "            '次数':[x for x in purchase.values()]})\n",
    "\n",
    "x = purchaseFrame['采购部门'] #坐标\n",
    "y = purchaseFrame['次数'] # 数据\n",
    "\n",
    "plt.figure(figsize=(20,8),dpi=100)\n",
    "\n",
    "plt.bar(range(len(purchaseFrame)),y)\n",
    "plt.xticks(range(len(purchaseFrame)),x)\n",
    "plt.ylim(0,150)\n",
    "plt.title('各部门采购频次统计表')\n",
    "plt.xlabel('采购部门')\n",
    "plt.ylabel('次数')\n",
    "\n",
    "for m,n in enumerate(purchaseFrame['次数']):\n",
    "    plt.text(m-0.1,n,\"%s\" %n)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2be568d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 推导式筛选采购次数大于15的\n",
    "\n",
    "purchaseTmp = dict((key,value) for key,value in purchase.items() if value > 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "74c47a62",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>采购部门</th>\n",
       "      <th>采购次数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>总务部</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>天琴中心</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>中山医学院</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>碧鼎技术部</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>测试中心</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>电子与信息工程学院（微电子学院）</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>物理学院</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>图书馆</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>医学院</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>测绘科学与技术学院</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>化学学院</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>总务处</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>网络与信息中心</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>电子与通信工程学院</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>智能工程学院</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>教育部食品工程研究中心</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>航空航天学院</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>大气科学学院</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>科学研究院</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>集成电路学院</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>海洋工程与技术学院</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>生命科学学院</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>材料科学与工程学院</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>土木工程学院</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                采购部门  采购次数\n",
       "8                总务部   136\n",
       "6               天琴中心    74\n",
       "0              中山医学院    66\n",
       "19             碧鼎技术部    51\n",
       "13              测试中心    44\n",
       "17  电子与信息工程学院（微电子学院）    44\n",
       "15              物理学院    41\n",
       "3                图书馆    36\n",
       "2                医学院    35\n",
       "12         测绘科学与技术学院    33\n",
       "1               化学学院    29\n",
       "7                总务处    28\n",
       "21           网络与信息中心    28\n",
       "18         电子与通信工程学院    27\n",
       "10            智能工程学院    25\n",
       "9        教育部食品工程研究中心    25\n",
       "22            航空航天学院    25\n",
       "5             大气科学学院    24\n",
       "20             科学研究院    21\n",
       "23            集成电路学院    18\n",
       "14         海洋工程与技术学院    17\n",
       "16            生命科学学院    17\n",
       "11         材料科学与工程学院    16\n",
       "4             土木工程学院    16"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchaseTmp2=pd.DataFrame({'采购部门':[x for x in purchaseTmp.keys()],\n",
    "            '采购次数':[x for x in purchaseTmp.values()]})\n",
    "purchaseTmp2.sort_values('采购次数',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0bc33c72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+8IEPZN68eWnatGm+853v5Mknn8w3vvGN7Lnnnhk/fnxxzZB//etfGy0rKisr06hRo1pPXhx88MH55S9/me985zv529/+tlXeQ58+ffLiiy9m7ty5mTdvXl577bVMmjQpyZvrsRx++OFp0KBBxo4dW2sB91deeSXjxo1L8v9Kk1deeSVJ0rp16yRvPr3y6quv5tZbb82XvvSlHHbYYVm+fHn+8Ic/ZN26dbXKlLlz5+aRRx7JiSeeWCx2fv/73+f666/P7rvvXjyvoqIi++yzzybfz/PPP5//+Z//yfDhw2uN//73v09FRUXuuOOOTV47derUfPzjH0/Lli3z/e9/Px/60IcyePDg3HbbbVm8eHG6deuWr33ta7nqqqvSqFGjXHXVVfn973+fsWPHbsFXvLRWrVpl//33T+vWrdOsWbP88pe/zAUXXJBrrrkmp59+em655ZbccccdadasWZKkf//+GTJkSPbZZ5984AMf2Go5AACgLjPNFwAA7ED22GOPjBgxIp06dUq3bt1SWVm5weLgb7f33nvntddey7p164prYbxVKPzxj38srk/y+9//PgMHDtzoPfr27ZsOHTpk4sSJ6dKly/t+D7vvvnutsmLBggUZOXJkevTokfbt22f16tX5j//4j/zXf/1X+vfvX2uR9LesWbMmyZvTkHXq1CmNGzfOHnvskWbNmuWyyy7LihUr0qNHjyxYsCBLly7N3XffnZYtW9YqU773ve/lYx/7WA477LDiPb/5zW/mnHPOye67715cS+Rf//pX2rZtW7zurfG3XHrppenRo0c+/elPF8dqampyww03ZPTo0Rk1alTuv//+nHzyyRu8j7eKmrcWmR80aFCOPPLI4ntevHhxrfVqkmz06/F2S5YsyXe/+91cccUVxWm+Nsf8+fNzwQUX5M4778zatWtz9NFHp6KiIhdddFEuvfTSnHDCCfnCF76Q5cuX54orrqj1tQQAgJ2dJ1MAAGAH86lPfSrHHHNMKioq0rBhw9xzzz0bnbbrF7/4RXHaqLcvKv7WuiFNmjRJs2bN0qxZszRu3LjWeiJJsmrVqjzzzDOZMGFCXn311Xed6uq9eOONN9KrV6+sWrUqt9xyS/Ff/n/rW99K48aN07Nnz7z00ksbXHfwwQfn4Ycfzq9//ev06tUrTzzxRK666qp8/etfz9ChQ3PZZZflF7/4RZ555pl873vfy4svvrjBmimXXXZZ8UmXJPnNb36TFStW5NJLL83q1avzgQ98IPXr18+f/vSnYumUpLjWSPLmGjR33nlnrbVdkuTCCy9MRUVFhg8fnssvvzyDBw/O9OnTN3gf3bp1y5QpU3LDDTfkq1/9avr165f/+7//S6FQyOuvv57nnnsuXbt23eyv5/Lly9OvX7/ceeedxbVU3s2yZcsyevToHHLIIfnrX/+aKVOm1Do+aNCgHH744TnssMOyatWq/OUvf8kRRxyx2ZkAAGBnoEwBAIAd2DsLkHd6e4nylk09UfDOc1euXJmjjjoqn/vc57LXXnvlvPPOe08Z3yph3rlw+8yZM9OjR4/MmTMnv/vd77L//vsXj33gAx/Ib3/727z22ms5+uijM2bMmFpPhLRo0SL77LNP7rzzznzyk59M7969c/bZZydJjjvuuFxyySX5j//4j+y1114ZPnx47rvvvg3WTGnTpk3at2+fBQsW5Iknnkjjxo1z9913p0WLFmnYsGEWLFiQadOmZfHixTn55JNz++23Z+rUqfnsZz+bJHnhhRfy+c9/PsOGDcvhhx+e5M2nW7785S/n5ptvzl133ZWGDRvmq1/9aj784Q/nuOOOy913313ra/Cvf/0rv//97/ODH/wgAwcOzJ577pmTTz4569evz9e//vUccMAB+fd///da19TU1OTiiy/O/Pnza42vXLkyv/rVr7Js2bI89thjxanP3s26dety++2355xzzsnTTz9dfC9J8tvf/jb/9m//lh/96Edp27Zt1qxZk5kzZ6ZQKGzWvQEAYGehTAEAgB3cwoULM2/evA22RYsWbfRfer9VSnTr1q24XsrJJ59cLD3eWo+kVatWGTduXO6///48++yz6dixY5LaT2aUsnLlynz3u9/NBRdckCTF9TVWrVqVUaNG5dhjj82KFSvyv//7vznqqKNqvXaSHH744fnDH/6QJk2a5LzzzssBBxxQnNJs6dKlGTRoUAYPHpzjjz8+kyZNys0335yrr756gxyVlZVZvXp1ZsyYUatgmD9/fn7wgx+kc+fOWbRoUZ544okccsghxeMTJ07Ml770pYwZMyaLFy/Ol7/85XziE59IdXV18fq99947V1xxRZI3p0075phjMmnSpNx7773FUqJevXq5/fbbc+KJJ2bAgAE544wzMnv27CTJxRdfnD59+uTee+/NUUcdlYceeijz58/PVVddlQkTJuTHP/5x8R4rV67MX//619x666357ne/u0GZMmPGjBxzzDGZPHly9thjj1rH3npaaWNatGiRJ598Mt/5znfSpEmTLF26NElyzjnn5NRTT83xxx+fNm3a5C9/+Uvat2+fj370o9lrr73yn//5n/nd7363iU8fAAB2MgUAAGCH1ahRo0KSTW5t27bd4Jp//etfhSSFP/7xj4WlS5cWli5dWrjzzjsLDRs2LBQKhcLUqVMLSQpLly7d6Gv+/e9/LyQpPProo++ar3v37oV27doVvvKVrxTWr19fKBQKhT//+c+FZs2aFfr06VP4v//7v1rnn3rqqYX+/fvXGnv99dcLn/3sZwt77LFH4fXXXy8UCoVC3759C507dy4sWrSoeN4PfvCDwsc+9rHi67zlE5/4RKFBgwaF+vXrFx588MFCoVAoLF68uNCjR49C8+bNC5dffnlh+fLlxfP/+Mc/Frp3715o2rRpYeLEicXx1157rXDMMccUOnToUFi8eHGhUCgU1q5dWygUCoUXXnih0KxZs8KJJ55YmDt37ia/Htddd11h9913Lzz00EOFQqFQeOONNwpvvPFGrXMuvvjiQmVlZWH8+PHFsVdffbXQrl27QpJCRUVF4bzzztvg3n/7298K1dXVG33dnj17Fi688MJN5nq7adOmFSoqKgrdunUr/PWvf93g+JNPPlkYNmxYoV27doV58+Zt1j0BAGBHV1EoeD4bAAB2VC+//HI++MEPpkmTJpt9zb/+9a8cc8wxueeee4pTSD322GP5/Oc/nxdeeCGPPfZYjj/++CxdujTNmjV7X/nevvD92z3//PM54IADNhj/2Mc+lgYNGuSBBx7Y4NiSJUuKi7HPmzcv69atS/v27YvHC4VC1q5du8HUZ1OnTs2LL76Y4447LnvvvXet+61bty6tWrWqdf7atWvzve99LwMHDkyHDh1qHVu2bFluv/32DBkyZIN88+bNy7777ruRr0Jt1dXVadGixSaPr169On//+9+3aK2Ure3BBx/Mxz72MYvMAwDA/0+ZAgAAAAAAUII1UwAAAAAAAEpQpgAAAAAAAJSgTAEAAAAAAChBmQIAAAAAAFBCZbkDbC/r16/Pa6+9lt133z0VFRXljgMAAAAAAJRRoVDI0qVLs88++6RevdLPnuwyZcprr72Wdu3alTsGAAAAAABQh8ydOzf77rtvyXN2mTJl9913T/LmF6V58+ZlTgMAAAAAAJTTkiVL0q5du2J/UMouU6a8NbVX8+bNlSkAAAAAAECSbNbSIBagBwAAAAAAKEGZAgAAAAAAUIIyBQAAAAAAoARlCgAAAAAAQAnKFAAAAAAAgBKUKQAAAAAAACUoUwAAAAAAAEpQpgAAAAAAAJSgTAEAAAAAAChBmQIAAAAAAFCCMgUAAAAAAKAEZQoAAAAAAEAJyhQAAAAAAIASlCkAAAAAAAAlKFMAAAAAAABKUKYAAAAAAACUoEwBAAAAAAAoQZkCAAAAAABQgjIFAAAAAACgBGUKAAAAAABACcoUAAAAAACAEpQpAAAAAAAAJShTAAAAAAAASlCmAAAAAAAAlKBMAQAAAAAAKEGZAgAAAAAAUIIyBQAAAAAAoARlCgAAAAAAQAnKFAAAAAAAgBKUKQAAAAAAACUoUwAAAAAAAEpQpgAAAAAAAJSgTAEAAAAAAChBmQIAAAAAAFBCWcuUhQsXpkOHDpkzZ85Gj/fu3Tvjx48v7s+cOTPdunVLq1atMnLkyBQKhe0TFAAAAAAA2GWVrUxZsGBB+vbtu8ki5ZZbbsmDDz5Y3K+pqUm/fv3StWvXTJs2LbNmzapVtAAAAAAAAGwLZStTBg4cmIEDB2702BtvvJERI0bk4IMPLo7df//9qa6uzrXXXptOnTrlqquuyk033bS94gIAAAAAALuoynK98Lhx49KxY8cMHz58g2MjRozIqaeempUrVxbHZsyYke7du6dp06ZJki5dumTWrFmbvH9NTU1qamqK+0uWLNl64QEAAAAAgF1G2Z5M6dix40bHH3300TzyyCO5+uqra40vWbIkHTp0KO5XVFSkfv36WbRo0UbvM2rUqLRo0aK4tWvXbuuFBwAAAAAAdhllXYD+nVatWpVhw4ZlzJgxad68ea1jlZWVadSoUa2xxo0bZ8WKFRu916WXXprq6uriNnfu3G2WGwAAAAAA2HmVbZqvjbnyyivTrVu39OnTZ4NjrVu3zsyZM2uNLV26NA0bNtzovRo1arRB+QIAAAAAALCl6lSZMnHixMyfPz8tW7ZMkqxYsSKTJk3K1KlTc8YZZ+RnP/tZ8dw5c+akpqYmrVu3LlNaAAAAAABgV1CnypTHH388a9euLe5fdNFF6d69e4YMGZKWLVumuro6EyZMyGc/+9mMHj06vXr1Sv369cuYGAAAAAAA2NnVqTJl3333rbXfrFmz7LHHHtljjz2SJOPGjcugQYMycuTIrFu3Lo899lg5YgIAAAAAALuQikKhUCh3iC3x6quvZtq0aenRo0f23HPPzb5uyZIladGiRaqrqzdY3B4AYEe3cOHCzJ49OwcddFD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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建DF\n",
    "# purchaseFrame=pd.DataFrame({'采购部门':[x for x in purchase.keys()],\n",
    "#             '次数':[x for x in purchase.values()]})\n",
    "\n",
    "x = purchaseTmp2['采购部门'] #坐标\n",
    "y = purchaseTmp2['采购次数'] # 数据\n",
    "\n",
    "plt.figure(figsize=(20,8),dpi=100)\n",
    "\n",
    "plt.bar(range(len(purchaseTmp2)),y)\n",
    "plt.xticks(range(len(purchaseTmp2)),x)\n",
    "plt.ylim(0,150)\n",
    "plt.title('各部门采购频次统计表')\n",
    "plt.xlabel('采购部门')\n",
    "plt.ylabel('采购次数')\n",
    "\n",
    "for m,n in enumerate(purchaseTmp2['采购次数']):\n",
    "    plt.text(m-0.1,n,\"%s\" %n)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "41ad2e42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>project_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>bid_section_id</th>\n",
       "      <th>bid_section_code</th>\n",
       "      <th>bid_section_code_mean</th>\n",
       "      <th>purchase_section_id</th>\n",
       "      <th>purchase_project_type</th>\n",
       "      <th>purchase_project_type_mean</th>\n",
       "      <th>supply_id</th>\n",
       "      <th>...</th>\n",
       "      <th>bid_price_chn</th>\n",
       "      <th>origin</th>\n",
       "      <th>tender_complete_ip</th>\n",
       "      <th>mac_address</th>\n",
       "      <th>hard_disk_seq</th>\n",
       "      <th>bid_apply_json</th>\n",
       "      <th>status</th>\n",
       "      <th>status_mean</th>\n",
       "      <th>create_time</th>\n",
       "      <th>modify_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F4F2593878214D5CBECB56A1F816A848</td>\n",
       "      <td>7AF0FF9F9C244A679BB07B5CF63AA1D8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1包</td>\n",
       "      <td>41CA14ABC13D4E3B81D0EEE2735B7562</td>\n",
       "      <td>A</td>\n",
       "      <td>货物</td>\n",
       "      <td>fdb5e529-2863-4515-a12e-ef7758d1b851</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>璃玥</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{\"name\":\"bundleId\",\"show\":false,\"label\":\"包\",\"...</td>\n",
       "      <td>1</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>12/8/2021 16:22:19</td>\n",
       "      <td>12/8/2021 16:22:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49FD050AF6764CDB99C5F8CFAB3ABD12</td>\n",
       "      <td>7AF0FF9F9C244A679BB07B5CF63AA1D8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1包</td>\n",
       "      <td>41CA14ABC13D4E3B81D0EEE2735B7562</td>\n",
       "      <td>A</td>\n",
       "      <td>货物</td>\n",
       "      <td>596c5ba2-f401-43ed-b7c2-fd9a7c2edaff</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>璃玥</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{\"name\":\"bundleId\",\"show\":false,\"label\":\"包\",\"...</td>\n",
       "      <td>1</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>12/8/2021 16:23:11</td>\n",
       "      <td>12/8/2021 16:23:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>947DAA5390BA4E2F8E2115D3EC0CD552</td>\n",
       "      <td>7AF0FF9F9C244A679BB07B5CF63AA1D8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1包</td>\n",
       "      <td>41CA14ABC13D4E3B81D0EEE2735B7562</td>\n",
       "      <td>A</td>\n",
       "      <td>货物</td>\n",
       "      <td>731a2acb-09a9-4e42-84ad-d4d3e1d0fbb1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>稻妻</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{\"name\":\"bundleId\",\"show\":false,\"label\":\"包\",\"...</td>\n",
       "      <td>1</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>12/8/2021 16:23:51</td>\n",
       "      <td>12/8/2021 16:23:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00C100655F0644878D894738EEC9337D</td>\n",
       "      <td>03883E99BA7446BDA6622B5DC496A85D</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1包</td>\n",
       "      <td>C6CFBFF3367A4AB999F653A0BE979DEA</td>\n",
       "      <td>B</td>\n",
       "      <td>工程</td>\n",
       "      <td>fdb5e529-2863-4515-a12e-ef7758d1b851</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>15/8/2021 11:33:04</td>\n",
       "      <td>15/8/2021 11:33:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2CE4BD607E294255ACB82D7545E1E746</td>\n",
       "      <td>03883E99BA7446BDA6622B5DC496A85D</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1包</td>\n",
       "      <td>C6CFBFF3367A4AB999F653A0BE979DEA</td>\n",
       "      <td>B</td>\n",
       "      <td>工程</td>\n",
       "      <td>bbb07678-a125-4ba5-8960-34ee609af39c</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>15/8/2021 12:46:19</td>\n",
       "      <td>15/8/2021 12:46:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8309</th>\n",
       "      <td>72F4F6EB67B242C28BB52E43E108C73B</td>\n",
       "      <td>A38C772C3A1E43E880F66721A2359243</td>\n",
       "      <td>e420faad-b3b9-4405-88a2-5a89fcf4f72c</td>\n",
       "      <td>68bd2b64-91cc-4346-8836-079008fae218</td>\n",
       "      <td>1</td>\n",
       "      <td>包组1</td>\n",
       "      <td>A90C72AB92B34708AA19DE4155DEB453</td>\n",
       "      <td>A</td>\n",
       "      <td>货物</td>\n",
       "      <td>6c3f6566-a274-4dae-842b-b82a034e0749</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>江苏开拓者救援装备有限公司</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{\"name\":\"bundleId\",\"show\":false,\"label\":\"包\",\"...</td>\n",
       "      <td>1</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>10/8/2023 09:35:34</td>\n",
       "      <td>10/8/2023 09:35:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8310</th>\n",
       "      <td>1094A1B795F54BB09497C217143C7BF7</td>\n",
       "      <td>A38C772C3A1E43E880F66721A2359243</td>\n",
       "      <td>e420faad-b3b9-4405-88a2-5a89fcf4f72c</td>\n",
       "      <td>68bd2b64-91cc-4346-8836-079008fae218</td>\n",
       "      <td>1</td>\n",
       "      <td>包组1</td>\n",
       "      <td>A90C72AB92B34708AA19DE4155DEB453</td>\n",
       "      <td>A</td>\n",
       "      <td>货物</td>\n",
       "      <td>7746d2d3-77c4-4fdb-b06c-2a37011d76a5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>广东平安消防实业有限公司、泰州开隆消防装备有限公司</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{\"name\":\"bundleId\",\"show\":false,\"label\":\"包\",\"...</td>\n",
       "      <td>1</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>10/8/2023 09:43:18</td>\n",
       "      <td>10/8/2023 09:43:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8311</th>\n",
       "      <td>64A386E8524C41748719FEB95DB08F31</td>\n",
       "      <td>10895A88C81C4FD0923CD515E90C66F5</td>\n",
       "      <td>9d2d1b97-4b88-4ec7-8595-d946809f30e9</td>\n",
       "      <td>a02717cc-1283-4cc5-acf9-71923a466c75</td>\n",
       "      <td>1</td>\n",
       "      <td>包组1</td>\n",
       "      <td>1B08542B9F5E4E178E7245011A43D73F</td>\n",
       "      <td>C</td>\n",
       "      <td>服务</td>\n",
       "      <td>55d53080-1bce-46e2-9092-81026df042ab</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{\"name\":\"bundleId\",\"show\":false,\"label\":\"包\",\"...</td>\n",
       "      <td>1</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>18/8/2023 09:35:58</td>\n",
       "      <td>18/8/2023 09:35:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8312</th>\n",
       "      <td>E8E642F851CA4D83B951C4CB4191A43D</td>\n",
       "      <td>10895A88C81C4FD0923CD515E90C66F5</td>\n",
       "      <td>9d2d1b97-4b88-4ec7-8595-d946809f30e9</td>\n",
       "      <td>a02717cc-1283-4cc5-acf9-71923a466c75</td>\n",
       "      <td>1</td>\n",
       "      <td>包组1</td>\n",
       "      <td>1B08542B9F5E4E178E7245011A43D73F</td>\n",
       "      <td>C</td>\n",
       "      <td>服务</td>\n",
       "      <td>7d323cc7-347d-4eb5-8ac8-da5ef4e88dcf</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{\"name\":\"bundleId\",\"show\":false,\"label\":\"包\",\"...</td>\n",
       "      <td>1</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>18/8/2023 09:36:05</td>\n",
       "      <td>18/8/2023 09:36:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8313</th>\n",
       "      <td>DF54715B5B96444FB6B5BCBC18B29177</td>\n",
       "      <td>10895A88C81C4FD0923CD515E90C66F5</td>\n",
       "      <td>9d2d1b97-4b88-4ec7-8595-d946809f30e9</td>\n",
       "      <td>a02717cc-1283-4cc5-acf9-71923a466c75</td>\n",
       "      <td>1</td>\n",
       "      <td>包组1</td>\n",
       "      <td>1B08542B9F5E4E178E7245011A43D73F</td>\n",
       "      <td>C</td>\n",
       "      <td>服务</td>\n",
       "      <td>36d697ab-1484-44eb-8827-60ad8cc1d690</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{\"name\":\"bundleId\",\"show\":false,\"label\":\"包\",\"...</td>\n",
       "      <td>1</td>\n",
       "      <td>数据有效</td>\n",
       "      <td>18/8/2023 09:36:35</td>\n",
       "      <td>18/8/2023 09:36:35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8314 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    id                        project_id  \\\n",
       "0     F4F2593878214D5CBECB56A1F816A848  7AF0FF9F9C244A679BB07B5CF63AA1D8   \n",
       "1     49FD050AF6764CDB99C5F8CFAB3ABD12  7AF0FF9F9C244A679BB07B5CF63AA1D8   \n",
       "2     947DAA5390BA4E2F8E2115D3EC0CD552  7AF0FF9F9C244A679BB07B5CF63AA1D8   \n",
       "3     00C100655F0644878D894738EEC9337D  03883E99BA7446BDA6622B5DC496A85D   \n",
       "4     2CE4BD607E294255ACB82D7545E1E746  03883E99BA7446BDA6622B5DC496A85D   \n",
       "...                                ...                               ...   \n",
       "8309  72F4F6EB67B242C28BB52E43E108C73B  A38C772C3A1E43E880F66721A2359243   \n",
       "8310  1094A1B795F54BB09497C217143C7BF7  A38C772C3A1E43E880F66721A2359243   \n",
       "8311  64A386E8524C41748719FEB95DB08F31  10895A88C81C4FD0923CD515E90C66F5   \n",
       "8312  E8E642F851CA4D83B951C4CB4191A43D  10895A88C81C4FD0923CD515E90C66F5   \n",
       "8313  DF54715B5B96444FB6B5BCBC18B29177  10895A88C81C4FD0923CD515E90C66F5   \n",
       "\n",
       "                                 source_id  \\\n",
       "0                                        1   \n",
       "1                                        1   \n",
       "2                                        1   \n",
       "3                                        2   \n",
       "4                                        2   \n",
       "...                                    ...   \n",
       "8309  e420faad-b3b9-4405-88a2-5a89fcf4f72c   \n",
       "8310  e420faad-b3b9-4405-88a2-5a89fcf4f72c   \n",
       "8311  9d2d1b97-4b88-4ec7-8595-d946809f30e9   \n",
       "8312  9d2d1b97-4b88-4ec7-8595-d946809f30e9   \n",
       "8313  9d2d1b97-4b88-4ec7-8595-d946809f30e9   \n",
       "\n",
       "                            bid_section_id  bid_section_code  \\\n",
       "0                                        1                 1   \n",
       "1                                        1                 1   \n",
       "2                                        1                 1   \n",
       "3                                        2                 1   \n",
       "4                                        2                 1   \n",
       "...                                    ...               ...   \n",
       "8309  68bd2b64-91cc-4346-8836-079008fae218                 1   \n",
       "8310  68bd2b64-91cc-4346-8836-079008fae218                 1   \n",
       "8311  a02717cc-1283-4cc5-acf9-71923a466c75                 1   \n",
       "8312  a02717cc-1283-4cc5-acf9-71923a466c75                 1   \n",
       "8313  a02717cc-1283-4cc5-acf9-71923a466c75                 1   \n",
       "\n",
       "     bid_section_code_mean               purchase_section_id  \\\n",
       "0                       1包  41CA14ABC13D4E3B81D0EEE2735B7562   \n",
       "1                       1包  41CA14ABC13D4E3B81D0EEE2735B7562   \n",
       "2                       1包  41CA14ABC13D4E3B81D0EEE2735B7562   \n",
       "3                       1包  C6CFBFF3367A4AB999F653A0BE979DEA   \n",
       "4                       1包  C6CFBFF3367A4AB999F653A0BE979DEA   \n",
       "...                    ...                               ...   \n",
       "8309                   包组1  A90C72AB92B34708AA19DE4155DEB453   \n",
       "8310                   包组1  A90C72AB92B34708AA19DE4155DEB453   \n",
       "8311                   包组1  1B08542B9F5E4E178E7245011A43D73F   \n",
       "8312                   包组1  1B08542B9F5E4E178E7245011A43D73F   \n",
       "8313                   包组1  1B08542B9F5E4E178E7245011A43D73F   \n",
       "\n",
       "     purchase_project_type purchase_project_type_mean  \\\n",
       "0                        A                         货物   \n",
       "1                        A                         货物   \n",
       "2                        A                         货物   \n",
       "3                        B                         工程   \n",
       "4                        B                         工程   \n",
       "...                    ...                        ...   \n",
       "8309                     A                         货物   \n",
       "8310                     A                         货物   \n",
       "8311                     C                         服务   \n",
       "8312                     C                         服务   \n",
       "8313                     C                         服务   \n",
       "\n",
       "                                 supply_id  ... bid_price_chn  \\\n",
       "0     fdb5e529-2863-4515-a12e-ef7758d1b851  ...           NaN   \n",
       "1     596c5ba2-f401-43ed-b7c2-fd9a7c2edaff  ...           NaN   \n",
       "2     731a2acb-09a9-4e42-84ad-d4d3e1d0fbb1  ...           NaN   \n",
       "3     fdb5e529-2863-4515-a12e-ef7758d1b851  ...           NaN   \n",
       "4     bbb07678-a125-4ba5-8960-34ee609af39c  ...           NaN   \n",
       "...                                    ...  ...           ...   \n",
       "8309  6c3f6566-a274-4dae-842b-b82a034e0749  ...           NaN   \n",
       "8310  7746d2d3-77c4-4fdb-b06c-2a37011d76a5  ...           NaN   \n",
       "8311  55d53080-1bce-46e2-9092-81026df042ab  ...           NaN   \n",
       "8312  7d323cc7-347d-4eb5-8ac8-da5ef4e88dcf  ...           NaN   \n",
       "8313  36d697ab-1484-44eb-8827-60ad8cc1d690  ...           NaN   \n",
       "\n",
       "                         origin  tender_complete_ip mac_address  \\\n",
       "0                            璃玥                 NaN         NaN   \n",
       "1                            璃玥                 NaN         NaN   \n",
       "2                            稻妻                 NaN         NaN   \n",
       "3                           NaN                 NaN         NaN   \n",
       "4                           NaN                 NaN         NaN   \n",
       "...                         ...                 ...         ...   \n",
       "8309              江苏开拓者救援装备有限公司                 NaN         NaN   \n",
       "8310  广东平安消防实业有限公司、泰州开隆消防装备有限公司                 NaN         NaN   \n",
       "8311                        NaN                 NaN         NaN   \n",
       "8312                        NaN                 NaN         NaN   \n",
       "8313                        NaN                 NaN         NaN   \n",
       "\n",
       "      hard_disk_seq                                     bid_apply_json status  \\\n",
       "0               NaN  [{\"name\":\"bundleId\",\"show\":false,\"label\":\"包\",\"...      1   \n",
       "1               NaN  [{\"name\":\"bundleId\",\"show\":false,\"label\":\"包\",\"...      1   \n",
       "2               NaN  [{\"name\":\"bundleId\",\"show\":false,\"label\":\"包\",\"...      1   \n",
       "3               NaN                                                NaN      1   \n",
       "4               NaN                                                NaN      1   \n",
       "...             ...                                                ...    ...   \n",
       "8309            NaN  [{\"name\":\"bundleId\",\"show\":false,\"label\":\"包\",\"...      1   \n",
       "8310            NaN  [{\"name\":\"bundleId\",\"show\":false,\"label\":\"包\",\"...      1   \n",
       "8311            NaN  [{\"name\":\"bundleId\",\"show\":false,\"label\":\"包\",\"...      1   \n",
       "8312            NaN  [{\"name\":\"bundleId\",\"show\":false,\"label\":\"包\",\"...      1   \n",
       "8313            NaN  [{\"name\":\"bundleId\",\"show\":false,\"label\":\"包\",\"...      1   \n",
       "\n",
       "      status_mean         create_time         modify_time  \n",
       "0            数据有效  12/8/2021 16:22:19  12/8/2021 16:22:19  \n",
       "1            数据有效  12/8/2021 16:23:11  12/8/2021 16:23:11  \n",
       "2            数据有效  12/8/2021 16:23:51  12/8/2021 16:23:51  \n",
       "3            数据有效  15/8/2021 11:33:04  15/8/2021 11:33:04  \n",
       "4            数据有效  15/8/2021 12:46:19  15/8/2021 12:46:19  \n",
       "...           ...                 ...                 ...  \n",
       "8309         数据有效  10/8/2023 09:35:34  10/8/2023 09:35:34  \n",
       "8310         数据有效  10/8/2023 09:43:18  10/8/2023 09:43:18  \n",
       "8311         数据有效  18/8/2023 09:35:58  18/8/2023 09:35:58  \n",
       "8312         数据有效  18/8/2023 09:36:05  18/8/2023 09:36:05  \n",
       "8313         数据有效  18/8/2023 09:36:35  18/8/2023 09:36:35  \n",
       "\n",
       "[8314 rows x 31 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "bid = pd.read_csv('D:\\\\data\\\\tdz_opening_bid_report.csv')\n",
    "bid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e22ba4bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " '广东省机电建筑设计研究院有限公司': 1,\n",
       " '广东省模拟医学科技有限公司': 3,\n",
       " '广东省源天工程有限公司': 3,\n",
       " '广东省灏联建筑工程有限公司': 13,\n",
       " '广东省电信规划设计院有限公司': 1,\n",
       " '广东省畅想云科技产业控股有限公司': 1,\n",
       " '广东省益盟进出口有限公司': 2,\n",
       " '广东省省装建设集团有限公司': 2,\n",
       " '广东省科安电梯工程技术有限公司': 1,\n",
       " '广东省第一建筑工程有限公司': 1,\n",
       " '广东省美术建设集团有限公司': 1,\n",
       " '广东省装饰有限公司': 19,\n",
       " '广东省金顶建设工程有限公司': 3,\n",
       " '广东知青搬家集团有限公司': 1,\n",
       " '广东禹固建筑工程有限公司': 1,\n",
       " '广东禾新建设工程有限公司': 14,\n",
       " '广东科信智能科技有限公司': 1,\n",
       " '广东科劳斯实验室系统科技股份有限公司': 3,\n",
       " '广东科明环境仪器工业有限公司': 1,\n",
       " '广东科能工程管理有限公司': 24,\n",
       " '广东科艺普实验室设备研制有限公司': 3,\n",
       " '广东科达信息技术有限公司': 1,\n",
       " '广东科达建设有限公司': 6,\n",
       " '广东穗建工程有限公司': 2,\n",
       " '广东穗晟建设工程有限公司': 12,\n",
       " '广东穗都建筑工程有限公司': 1,\n",
       " '广东空港建设有限公司': 23,\n",
       " '广东立佳实业有限公司': 1,\n",
       " '广东立胜综合能源服务有限公司': 2,\n",
       " '广东竹纶文化传媒有限公司': 5,\n",
       " '广东筑兴建设工程有限公司': 3,\n",
       " '广东粤冠建设有限公司': 2,\n",
       " '广东粤昊建筑有限公司': 8,\n",
       " '广东粤腾建设有限公司': 23,\n",
       " '广东粤诚建筑工程有限公司': 1,\n",
       " '广东精点数据科技股份有限公司': 12,\n",
       " '广东精艺建设集团有限公司': 4,\n",
       " '广东红利建设工程有限公司': 8,\n",
       " '广东红谷组建设有限公司': 1,\n",
       " '广东纳睿雷达科技股份有限公司': 1,\n",
       " '广东纵横建筑设计有限公司': 1,\n",
       " '广东维正科技有限公司': 2,\n",
       " '广东绿景建设有限公司': 1,\n",
       " '广东美高贸易有限公司': 7,\n",
       " '广东耀安建设工程有限公司': 4,\n",
       " '广东耀安消防设备工程有限公司': 7,\n",
       " '广东耀恒建设工程有限公司': 15,\n",
       " '广东聚标环保科技有限公司': 1,\n",
       " '广东腾信建设工程有限公司': 1,\n",
       " '广东腾大建设集团有限公司': 1,\n",
       " '广东腾泰建设有限公司': 7,\n",
       " '广东腾盛建设有限公司': 1,\n",
       " '广东腾龙建设有限公司': 1,\n",
       " '广东臻正建设有限公司': 1,\n",
       " '广东航天宏图信息技术有限公司': 1,\n",
       " '广东艺通装饰工程有限公司': 1,\n",
       " '广东艾发信创设计院有限公司': 1,\n",
       " '广东艾维特科技有限公司': 2,\n",
       " '广东英祥建设工程有限公司': 23,\n",
       " '广东茂松建工集团有限公司': 18,\n",
       " '广东荣庆建筑工程有限公司': 27,\n",
       " '广东荣鸿建设有限公司': 1,\n",
       " '广东蒙晟建设工程有限公司': 11,\n",
       " '广东蓝天智通科技有限公司': 1,\n",
       " '广东蓝水特卫保安服务有限公司': 1,\n",
       " '广东融邦惠众建设有限公司': 1,\n",
       " '广东裕兆丰建设有限公司': 27,\n",
       " '广东裕方建设工程有限公司': 16,\n",
       " '广东裕盛建设有限公司': 3,\n",
       " '广东裕达建设集团有限公司': 11,\n",
       " '广东豪源建设有限公司': 1,\n",
       " '广东豪龙建设有限公司': 6,\n",
       " '广东贤兴建设工程有限公司': 7,\n",
       " '广东超丰机电工程有限公司': 3,\n",
       " '广东越维信息科技有限公司': 2,\n",
       " '广东越良建筑工程有限公司': 11,\n",
       " '广东轩辕网络科技股份有限公司': 5,\n",
       " '广东辰光建设有限公司': 23,\n",
       " '广东远乔工程有限公司': 3,\n",
       " '广东远胜建设有限公司': 8,\n",
       " '广东遂商建设投资集团有限公司': 13,\n",
       " '广东邦拓建设工程有限公司': 3,\n",
       " '广东邦晖建筑工程有限公司': 7,\n",
       " '广东邦的机电设备有限公司': 6,\n",
       " '广东金中海建设工程有限公司': 25,\n",
       " '广东金建建筑股份有限公司': 15,\n",
       " '广东金弘达建设工程有限公司': 1,\n",
       " '广东金朋家具制造有限公司': 1,\n",
       " '广东金泰建设工程有限公司': 1,\n",
       " '广东金灿建设有限公司': 6,\n",
       " '广东金玉旅运有限公司': 1,\n",
       " '广东金辉华集团有限公司': 1,\n",
       " '广东金运环境建设有限公司': 10,\n",
       " '广东金锐建设有限公司': 13,\n",
       " '广东鑫越建设工程有限公司': 27,\n",
       " '广东铭太信息科技有限公司': 2,\n",
       " '广东铭禧建设有限公司': 17,\n",
       " '广东银旅通信息网络发展有限公司': 2,\n",
       " '广东银泰建筑工程有限公司': 13,\n",
       " '广东锐阳建设工程有限公司': 11,\n",
       " '广东长博建设工程有限公司': 31,\n",
       " '广东隆建工程有限公司': 5,\n",
       " '广东雄业建设工程有限公司': 1,\n",
       " '广东集富实业有限公司': 3,\n",
       " '广东雍朋智能科技有限公司': 1,\n",
       " '广东青藤环境科技有限公司': 1,\n",
       " '广东顺德禾庄能源科技有限公司': 5,\n",
       " '广东飞鑫建设有限公司': 1,\n",
       " '广东骏业建设有限公司': 9,\n",
       " '广东骏亨建设有限公司': 1,\n",
       " '广东骏天建设工程有限公司': 8,\n",
       " '广东骏森建设工程有限公司': 19,\n",
       " '广东高德智能建筑股份有限公司': 1,\n",
       " '广东高校科技成果转化中心': 3,\n",
       " '广东高达建设工程有限公司': 23,\n",
       " '广东鸿升建设有限公司': 12,\n",
       " '广东鸿泰建设有限公司': 4,\n",
       " '广东鸿越建设有限公司': 12,\n",
       " '广东鹏展建筑工程有限公司': 5,\n",
       " '广东鹏鉴建设工程有限公司': 16,\n",
       " '广东龙元建设有限公司': 21,\n",
       " '广州一业建筑工程有限公司': 1,\n",
       " '广州一凌智能科技股份有限公司': 2,\n",
       " '广州一区科技有限公司': 1,\n",
       " '广州一品云信息科技有限公司': 6,\n",
       " '广州一城建筑工程有限公司': 28,\n",
       " '广州一洲信息技术有限公司': 1,\n",
       " '广州一诺仪器科技有限公司': 1,\n",
       " '广州万博建筑工程有限公司': 24,\n",
       " '广州三特医药科技有限公司': 2,\n",
       " '广州三羊建设工程有限公司': 14,\n",
       " '广州三闪特科仪器有限公司': 1,\n",
       " '广州三飞创新科技有限公司': 1,\n",
       " '广州上宏器技术服务有限公司': 1,\n",
       " '广州东林建设工程有限公司': 12,\n",
       " '广州东盛信息技术有限公司': 1,\n",
       " '广州中今科技有限公司': 2,\n",
       " '广州中卡智能科技有限公司': 1,\n",
       " '广州中大中鸣科技有限公司': 1,\n",
       " '广州中太建筑安装工程有限公司': 18,\n",
       " '广州中炬圣火健康科技有限公司': 1,\n",
       " '广州中琛科技有限公司': 1,\n",
       " '广州中电一线科技有限公司': 1,\n",
       " '广州中科诺泰技术有限公司': 2,\n",
       " '广州中软信息技术有限公司': 2,\n",
       " '广州中远海运船务代理有限公司': 2,\n",
       " '广州中远海运船舶工程有限公司': 3,\n",
       " '广州中长康达信息技术有限公司': 7,\n",
       " '广州义金生物科技有限公司': 1,\n",
       " '广州之友仪器科技有限公司': 1,\n",
       " '广州乔尔实验室设备科技有限公司': 1,\n",
       " '广州乾元科学仪器有限公司': 1,\n",
       " '广州云帆智能科技有限责任公司': 2,\n",
       " '广州云蝶科技有限公司': 1,\n",
       " '广州五州科技有限公司': 1,\n",
       " '广州仪速安电子科技有限公司': 5,\n",
       " '广州伊铭科技有限公司': 5,\n",
       " '广州众粤市政园林设计工程有限公司': 1,\n",
       " '广州众纳科技有限公司': 1,\n",
       " '广州优路加信息科技有限公司': 1,\n",
       " '广州优飞信息科技有限公司': 1,\n",
       " '广州伟思创科技有限公司': 2,\n",
       " '广州佰能信息科技有限公司': 2,\n",
       " '广州佳兆建筑装饰工程有限公司': 1,\n",
       " '广州佳创电梯有限公司': 1,\n",
       " '广州佳禾科技股份有限公司': 1,\n",
       " ...}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2Dict(bid['bidder'])"
   ]
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